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Record W2803565178 · doi:10.2106/jbjs.rvw.17.00153

Cannabinoids in the Management of Musculoskeletal Pain

2018· review· en· W2803565178 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJBJS Reviews · 2018
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMental Health Research CanadaUniversity of TorontoMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineArthritisPhysical therapyPain managementCannabisMusculoskeletal painPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

* The purposes of the present scoping review were to identify (1) the available studies regarding the efficacy of cannabinoids for the management of musculoskeletal pain and related conditions and (2) the knowledge gaps and opportunities in this area of research. * There is little high-quality evidence for medical cannabis in the core orthopaedic areas of arthritis, postoperative pain, back pain, and trauma-related pain. * The “best available” evidence suggests cannabis can be effective for managing arthritis pain, back pain, and trauma-related pain, although the quality of the evidence is poor. * Evidence regarding the use of cannabinoids for the management of postoperative pain is mixed. * Research on pain control in patients with arthritis, conditions related to the spine, and traumatic injuries represents major under-represented areas of study for the role of cannabinoids, and high-quality Level-I studies are needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.417
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it